class_docstr: Joint 3D Face Reconstruction and Dense Alignment with Position Map Regression Network
  Args:
      is_dlib(bool, optional): If true, dlib is used for detecting faces.
      prefix(str, optional): If run at another folder, the absolute path is needed to load the data.
class_docstr_tok: ["Joint","3D","Face","Reconstruction","and","Dense","Alignment","with","Position","Map","Regression","Network","Args",":","is_dlib","(","bool",",","optional",")",":","If","true",",","dlib","is","used","for","detecting","faces",".","prefix","(","str",",","optional",")",":","If","run","at","another","folder",",","the","absolute","path","is","needed","to","load","the","data","."]
code: def dlib_detect(self, image):
      return self.face_detector(image, 1)
  
file_path: PaddleHub/modules/image/image_processing/prnet/api.py
imports: import os
  from time import time
  import numpy
  import paddle
  from skimage.io import imread
  from skimage.io import imsave
  from skimage.transform import estimate_transform
  from skimage.transform import warp
  from .predictor import PosPrediction
label_desc: Use image node_id 3 for calling the PRN obj's underlying member method code with example usage: obj.dlib_detect(image) and returns: self